NLP: Polysynthetic morphology extraction. Greenlandic is a difficult language to work with because it forms words by adding many parts (called morphemes) together. This project builds a tool for identifying where those morphemes in a word in a sentence. It might use rules, or a deep learning based method, perhaps using character-level convolutional neural nets, or neural attention.

NLP: Clinical event recognition. Lots of information about patients and their health is kept in clinical notes. Clinical note technology is advanced for English but not so for Danish - a shame, because we have great digitalisation. This mini-project helps close that gap by building tools for automatically extracting clinical events (like surgeries, heart attacks, medication changes) from Danish clinical notes.

ML: Gemstone valuation. Gemstones are tough to value, due to their hugh variance. This project involves collecting a lot of data on one or two gem types, and building a model of gem stone value based on various features of the stone - e.g. color richness, cut depth, cut quality, mine of origin, weight, and so on. A working model will perform automatic estimation of gem value, an otherwise expensive service.

NLP: Time recognition. We should be able to connect dates and day mentions to calendars. This is a tougher task than it looks to automate (e.g. on what day is pinse 2014?), and needs to be done for each language. Here we'll use ISO-TimeML to process for Danish (or another non-English language) and then build a neural network for recognizing times automatically.

NLP: Stance detection. Fake news detection currently relies on knowing the attitude that people talking on social media are expressing towards and idea. Figuring this out is called stance detection. This project builds a stance detection system for Danish or a non-English language of your choice, over social media data.

NLP: Neural Eliza. There's an old program, Eliza, that lets you talk to the computer. It was far ahead of its time. This project builds a neural network that functions as Eliza does. Meet Eliza in javascript

For details and research specialisations, visit the webpage: nlp.itu.dk

ITU is an agile, non-traditional, expanding university. I think of it as the startup of unis. Projects are easy to take on and novel ideas have plenty of instituional support. It achieves one of the highest rates of external funding per faculty member in the country, and the highest rate of female:male student applicants (29% of Bachelor in Software Development course applicants were female in 2018). The building is situated just between the national broadcaster, DR, and the KUA campus, and has a professional, light and spacious feel.

Fake news, veracity & stance - continuing work from the PHEME project: how do we determin veracity of claims made on the web? What behaviours exist around news? How do we know that the data your system is processing is geniune and accurate?